An Integral Approach to Active Learning in Mathematics
Bibliographic record
Abstract
In this chapter we examine the notion of “active learning” through Wilber's Integral AQAL Model and through two learning models based on AQAL. Our examination of Edwards' integral learning and Renert and Davis' five stages of mathematics, results in a multi-perspective, multi-level notion of “active learning”. We demonstrate, through the development of a rubric to gauge students' “activeness”, the complexity of what is involved in the teaching and learning process when one becomes mindful of the perspectives and levels (AQAL) that are present for every student. Several episodes of learning are used to show how each theoretical model applies, and an extended episode, which illustrates a student's repair strategy on a mathematically erroneous concept, is used to illustrate the analysis of the extent of active learning. The chapter concludes with a discussion of how the rubric of active learning, along with the four continua, can help teachers be mindful of the multiple perspectives that influence learning.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".